Open Access Open Access  Restricted Access Subscription or Fee Access

Identifying the Rice Diseases Using Classification Techniques

A. Nithya, Dr.V. Sundaram

Abstract


Rice disease identification is one of the main issue of the country. The essence of the paper is identifying the rice disease in initial stage. This paper going to be create ready reckoned of the farmers. The main advantage of the paper is easily identifying the disease and gives the better solution for the farmers. It is the process which includes defining and redefining problems, formulating hypothesis, collecting, organizing and evaluating data; making deductions and reaching conclusions and at last presenting it in a detailed, accurate manner. This paper mainly focuses on concepts of data mining such as Classification, Decision Trees, and Neural Networks. A disease is an abnormal condition that injures the plant or causes it to function improperly. Diseases are readily recognized by their symptoms - associated visible changes in the plant. The organisms that cause diseases are known as pathogens. Many species of bacteria, fungus, nematode, virus and mycoplasma-like organisms cause diseases in rice. Disorders or abnormalities may also cause by abiotic factors such as low or high temperature beyond the limits for normal growth of rice, deficiency or excess of nutrients in the soil and water, pH and other soil conditions which affect the availability and uptake of nutrients, toxic substances such as H2S produced in soil, water stress and reduced light.. However, here we will cover only the common diseases of rice those cause by pathogen.

Keywords


Decision Trees, Classification, Data Mining, Neural Network

Full Text:

PDF

References


A survey of data mining techniques applied to agriculture A.Mucherino.Petraq papajorgji-P.M.Paradalos

Classification rules for Indian Rice diseases- A.Nithya,Dr.V.Sundaram

Breiman, L., Friedman, J. H., Olshen, R., and Stone, C. (1984). Classification and Regression Trees. Wadsworth, Belmont, CA.

Hand,D.(1997). Construction and Assessment of Classification Rules. John Wiley & Sons, Chichester

Ying Lu, Jiawei Han, “Cancer Classification Using Gene Expression Data”, Information Systems vol. 28 issue 4, Elsevier Science Ltd., Oxford,UK, 2003, pp. 243 – 268.

Patricia L.Dolan, Yang Wu, Linnea K. Ista, Robert L. Metzenberg, Mary Anne Nelson, Gabriel P. Lopez, “Robust and efficient synthetic method for forming DNA microarrays”, PubMed Central, Oxford University Press, USA, 2001.

leaves recognition using back propagation neural network-advice for pest & disease control on crops. M. S. Prasad Babu & B.Srinivasa Rao


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.